This document discusses Foursquare's data analytics capabilities and team. It describes how Foursquare is able to perform massive intersection queries on user check-in and venue similarity data in under 100ms. It also outlines Foursquare's goals for their internal analytics dashboard, including a simple interface, scalability, and supporting most use cases fast while being cheap. The document notes that Foursquare's data team consists of a mix of analytics, science, and engineering skills and provides some references on building effective data science teams. It concludes by inviting the reader to join Foursquare's hiring opportunities.

11. Brooklyn San Francisco Australia

13. repeatcheckin %

14. The Adjustment Bureau

17. Explore

20. Engineering

21. Our Stack

23. Massive Intersection Queries select * from checkins where user in friends and venue in nearby select * from similarities where venue1 in myHistory and venue2 in nearby

24. < 100ms

25. Analytics

28. Goals Simple Interface Scales with our data Cheap (free) Supports 90% use cases Fast

29. Our Internal Dashboard EMR

30. The Team

32. The Anatomy of a Data Team Analytics (Stats) Science (ML) Engineering (CS) Mix of all of these!

33. Building a Data Team It’s hard Good references: http://radar.oreilly.com/2011/09/building-data-science-teams.html http://mathbabe.org/2011/09/25/why-and-how-to-hire-a-data-scientist-for-your-business/

34. Join us! foursquare is hiring www.foursquare.com/jobs Justin Moore @injust justin@foursquare.com